Monocular depth estimation has drawn widespread attention from the vision community due to its broad applications. In this paper, we propose a novel physics (geometry)- driven deep learning framework for monocular depth estimation by assuming that 3D scenes are constituted by piecewise planes. Particularly, we introduce a new normal distance head that outputs pixel-level surface normal and plane-to-origin distance for deriving depth at each position. Meanwhile, the normal and distance are regularized
by a developed plane-aware consistency constraint. We further integrate an additional depth head to improve the robustness of the proposed framework. To fully exploit the strengths of these two heads, we develop an effective contrastive iterative refinement module that refines depth in a complementary manner according to the depth uncertainty. Extensive experiments indicate that the proposed method exceeds previous state-of-the-art competitors on the NYUDepth- v2, KITTI and SUN RGB-D datasets. Notably, it
ranks 1st among all submissions on the KITTI depth prediction online benchmark at the submission time.
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Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - MTC Programmatic Funds grant
Grant Reference no. : M23L7b0021